Maybe you can say a little more about your BI tool and how people would use it if they're not SQL or Python programmers. This is another X-Force webinar, one of a series on data in Salesforce and data outside of Salesforce. There's another question from the audience. I think one thing about working here is there's never a shortage of projects. So they would have to know SQL and a little bit of Python to do that. ) Mandy Gu: (32:53) It's not just operational use cases, but we don't actually use it for analyzing financial data. We also have to look and make sure that our metrics performance metrics are reliable. ) So the data science part is in Toronto. Or did you write your own SQL parser from scratch? ) Huh? I think we're just trying to get a feel for how well they think and how well they problem-solve. Mandy Gu: (29:53) That's an excellent question. Wealthsimple started out as an investment platform, which provided this nice, really easy way of investing money. If you go on the Wealthsimple website, it does give a breakdown of how we pick out the securities for investments, and machine learning is not part of the process. Wealthsimple | 40,006 followers on LinkedIn. Engineering; Product Management; Trust; Work type. However, the test ensures that we get well aligned with the stakeholders on things needed - and being a part of that process. The company has raised $78 million in capital. Pre-COVID, did you have remote in place? I guess in terms of tips and tricks, I find that by building a lot of tooling and giving everyone on the team the confidence to deploy these pipelines, the process gets greatly accelerated. Leonard Lindle: (38:17) You're eating the dog food. That's pretty cool. The fintech space is crowded, but we are well positioned for continued explosive growth and success. I can say that we do a lot of experiments. Anyone can just go in, create their own tripwires, and indicate the cadence and the scheduled interval. Mandy introduces viewers to Wealthsimple's versatile SQL "tool belt," and brings us through the Wealthsimple production workflow. There's the understanding that if there's anything they don't know that they can pick it up on the job. Powerful financial tools to help you grow and manage your money. We're all doing a bunch of things. So there are definitely a lot of very interesting projects. Right, because you don't employ any drag and drop or simple-to-use ETL tools. My team's responsibility is more like loading that data into the data warehouse. Simple, right? Questwealth Portfolios vs Wealthsimple: How it works? I think that was one of the really nice things about Waterloo was getting that work experience. One of the attendees asked if there is a way nontechnical members of the data team can push data into your data warehouse? To me, I think it takes a lot of time. Wealthsimple’s personnel is composed of designers, data scientists, and software engineers who have previously worked at big corporations such as Google, Apple, and Amazon. It sounds like you have a really cool team. Is that a requirement that the tool belt must get used before somebody can push something into production? Did you take a course or a program in machine learning? Most of the machine learning models start with a business problem, and we work on it from conception. Leonard Lindle: (10:37) I know it's not your project, but do you know if you leveraged any open-source libraries or anything else to build on top of it? I have not worked too much on it. For instance, if something has to get sent to an SFTP server, we don't want to like to send it to the room. So do you guys work together in Toronto, or do you have remote in place? I think that's about everything. We're just about at the 45-minute mark here - do you have any last words or any more words of wisdom and advice for our audience? ) So at Wealthsimple, we are huge on SQL - everyone on the company is. What kind of a cadence are you running on in terms of putting models out? ) We want to enforce good patterns. Since then, there have been a lot of efforts to simplify parallelizing this process. She's going to join us and tell us something about their data pipeline and about a couple of interesting innovations that her team has put together at her … Today, we have somebody who has data outside of Salesforce, Mandy Gu. There's usually a pair programming and problem-solving segment as well. As of August 2019, the firm holds over C$5 billion in assets under management. Mandy is a data science scientist at Wealthsimple. We use it a lot for our ad hoc analysis, but many people at Wealthsimple are very well-versed in SQL - so we have many people building their own dashboards using the tool. I got thrilled to see that because a good portion of my money is with Wealthsimple. Data Scientist @ Wealthsimple Toronto, Canada Area 500+ connections. Leonard Lindle: (34:15) Here's another one - was your favorite co-op experience? This is another X-Force webinar, one of a series on data in Salesforce and data outside of Salesforce. Mandy Gu: (28:26) We have a nice local development setup that they can spin up - a very similar environment to our production environment. What's the interview process like? They play an advisory role in Wealthsimple's investment management process and serve as a sounding board for Wealthsimple's … That's great. So our BI tool actually does support like a Python functionality if you want to import the data as a data frame and work with it. To me, I think it takes a lot of time. When we were talking earlier, you said you also created some kind of a SQL - I would call it a code analyzer, but you can tell me what you call it. Here's another one - was your favorite co-op experience? ) We do a little bit of everything, from data science to data engineering to a little bit of software stuff. Leonard Lindle: (20:28) Another question from the audience: where have you applied machine learning models? Are you happy with your move to the production workflow? That's an excellent question. What kind of a cadence are you running on in terms of putting models out? I wouldn't say there are too many challenges other than one other thing: being generalists, we're kind of getting pulled into every direction, and the context switching can be a little hectic at times. Leonard Lindle: (38:33) So just out of curiosity about Wealthsimple - is there machine learning or some kind of insight applied to the client about what sort of investment products would be right for them does your team have anything to do with that? Wealthsimple Inc. is a Canadian online investment management service focused on millennials. Mandy Gu: (26:58) The most time-consuming part, I find, is understanding the business problem. Typically, though, we're responsible up until that point. It's hard to say my favorite. We have five data scientists and a software engineer. You have locations in New York, London, and Toronto, and you're associated with the Toronto location. Just knowing SQL, you can write your own queries with the BI tool, and the BI tool can help visualize and perform straightforward analytics on the SQL output. From there, we learn about the advanced data pipeline at Wealthsimple and their extensive use of Airflow within their data stack. 2014. Mandy Gu: (35:35) There isn't a co-op requirement. Mandy Gu: (15:14) You know, we oversee the data warehouse, and we monitor like the BI tool as well. Leonard Lindle: (16:39) So the data science part is in Toronto. Mandy Gu: (20:37) We do a lot of AB testing. Actually, in an article released a while ago - with all of the volatility in the marketplace, the Wealthsimple portfolio was actually one of the ones that performed really well. I don't remember the exact numbers, but we were able to see a huge lift in getting the transfer to the right place after implementing the model, as opposed to the client selection. We talk to them and answer any questions they may have. We often use the SQL to about functionality for things like schema rewrites, whenever there are upstream changes in the data columns or the data names. -Wealthsimple has extremely lofty ambitions, but because of the sheer talent here, achieving those ambitions is realistic. If there were issues with the data, they would most often fall into the engineering teams' domain and their stead. ) Are you happy at five, or do you think you're looking for other people to tackle other company challenges? ) Our models also add kind of a lot - our more important models are services on their own. Core Operations; Technical Teams. You might just say it's the future of the finance industry; Wealthsimple is gaining traction as the new, simple, and affordable way for almost anyone to start investing. I have a major in statistics. Toronto. Headquarters. I focused on backend API creation, maintenance, and enhancement, with an … I guess a little bit about myself, first. So we try to make everything as self-serve as possible. Wealthsimple builds a diversified portfolio of ETFs on the investors' behalf and guides them in achieving their financial goals. If it goes through your pipe, through your QA checks, it's not going to break anything. Any advice you can give new team members, that kind of thing? ) This is not just machine learning, but data engineering. It's been great speaking here and answering and engaging with the audience. ) There are a lot of cool things going on. That does give us the confidence to develop faster. I think it's just a process of exposing yourself to more things and picking them up as you go. First, there would be a call with the hiring manager, and past that call, we'd send them a technical assessment. We use Airflow a lot. One of our data scientists is great with this kind of stuff—he kind of runs our experiments. I think that I really like the idea of the different hooks and the different operators and how the logic is relatively clear. Do you do a lot of AB testing on your website? Wealthsimple is backed by a team of world-class financial experts and the best technology talent. Today, we have somebody who has data outside of Salesforce, Mandy Gu. This would get abstracted entirely from the process. Some of these products include a commission for your trading platform and a high-interest savings account. I think we're just trying to get a feel for how well they think and how well they problem-solve. Mandy Gu: (22:59) So one of the first models that I worked on when I first started was on an accelerated institutional transfer. Mandy Gu: (16:25) So, the data team is mostly in Toronto. If it goes through your pipe, through your QA checks, it's not going to break anything. Mandy Gu: (34:38) I did six co-ops while I was at Waterloo. So this has historically been a huge client pain point because of just how long it takes. Then you have to run it back in through your pipeline to see if the experiment worked and all that. Leonard Lindle: (14:19) Right, because you don't employ any drag and drop or simple-to-use ETL tools. We also have to look and make sure that our metrics performance metrics are reliable. If you go on the Wealthsimple website, it does give a breakdown of how we pick out the securities for investments, and machine learning is not part of the process. ) So we did leverage a lot of those open-source frameworks out there. ) Your ETL is SQL and Python period. There are a lot of interesting projects that kind of have gotten prioritized for these upcoming quarters. What are your internal rules on that? Thankfully, we've not yet encountered a case where we're in the middle of developing something and then realized that the model is not up to standard. Wealthsimple is a digital investment service that uses technology to make investing simpler, smarter and low-cost. So nobody's sitting there just worried about breaking the build of the software engineer. I think one of the other things a lot of companies do is write views for end-users. Do your analysts use any kind of data visualization tools like Tableau or something like that? Whether it is through the onboarding phase or through getting money into Wealthsimple. They can run this pipeline from end to end. So you have a real complex joint or something fancy going on. In terms of monitoring, tripwires are one of the things that we do use for monitoring. You didn't write your own parser from scratch. All; Business Teams. So, "easy" is one of your value profits - "simple" is right in your name. Leonard Lindle: (39:14) We're just about at the 45-minute mark here - do you have any last words or any more words of wisdom and advice for our audience? It's a pretty easy decision just to deprecate the model and revert a lot of the aspects. Do you have any tips or tricks on how to save time building your pipelines? ) Instead of having clients make these decisions, we would actually use the models to make those decisions. We have a pretty standard machine learning workflow getting set up, and a lot of that leverage is on Airflow as well. 64 salaries for 34 jobs at Wealthsimple in Toronto, ON, Canada Area. Or do they have to know SQL? Leonard Lindle: (22:45) Can tell us about a time when you think your machine learning really brought something helpful to the platform, the application, or your understanding of your client behavior? Mandy Gu: (37:54) Probably not adjusting any models because we don't really have any models dependent on the data. Wealthsimple is backed by a team of world-class financial experts and some of Silicon Valley's best technology talent. So I had the opportunity to get involved in the development and the production of the data products from the get-go. ) To begin the podcast, Mandy introduces us to Wealthsimple, going over the basics of this intriguing, millennial-focused startup. Wealthsimple provides you with world-class, long-term investment management… Can tell us about a time when you think your machine learning really brought something helpful to the platform, the application, or your understanding of your client behavior? ) We're confident that in our testing framework; if that passes, it means this is a really good state to go. Its staff is made up of software engineers, designers and data scientists who have previously worked at such companies as Amazon, Google and Apple. Portfolio; About; LinkedIn; Email; Wealthsimple Software Engineer Intern (Winter 2019) As a member of the Platform Engineering team, I mainly worked on back-office systems with large amounts of transactional data. Do you do a lot of AB testing on your website? ) Your feedback has been sent to the team and we'll look into it. The process took 4 weeks. Yeah, we do a lot of our machine learning models. We have a pretty standard machine learning workflow getting set up, and a lot of that leverage is on Airflow as well. I'm really excited to be here. Having that certainly makes testing a lot easier and also takes away the worry that they'll break something when they test. Free interview details posted anonymously by Wealthsimple interview candidates. Typically, though, we're responsible up until that point. There is never a shortage of projects, and there is a lot of really exciting work. I do think that our interview process is a little bit more abstracted and a little bit more detached from our day to day operations. Leonard Lindle: (37:04) Is your work environment fast-paced? Well, thank you for telling us some more about Wealthsimple. Before working at Wealthsimple, I worked a while at a startup doing conversational AI. One wire is a check that evaluates to either true or false. The process started with a phone screening followed by a case and a full loop in person interview.The case was super broad, it was basically WS giving me a generic dataset and asking me to analyze, instead of having a problem that could be solved with data. There's a lot of flexibility to modify your own hooks and your own operators for your specific use case. Structured data vs unstructured data – what are the key differences? Are you going to be adjusting any models due to that? How do you hire? Are you planning on growing your team? This deck would orchestrate, pulling the data from where it needs to get pulled from running the training script. We have tripwires around things like model performance. So if I'm one of your SQL programmers and I'm in charge of a data pipeline in Airflow, I just have to write a SQL statement that evaluates true or false that tells me something about my pipeline. I believe the lift was actually close to 20%. I applied through an employee referral. So one previous issue that a lot of members of the team had was it was taking too long. We also use Airflow to manage a lot of our reporting jobs. If it is, we will upload a model asset somewhere, and from that location, this model asset would get picked up by our model server for people to use the latest version of the model. ) Is it good enough? We try to keep up with things. I think there are plans definitely to grow the team, and people recognize that the team does good work, and there's a need for more. Mandy Gu: (26:14) We certainly don't expect that they would be familiar with our entire tech stack or everything that we use. Want to learn more? Actually, some programs do have a co-op requirement, but for mine and a lot of others, it's optional. Often, that data is not easily accessible, and that's another rabbit hole of "how can I get this data?" The advanced data pipeline process team, you had a couple of learning. 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